Measurements and Characterization of 5G Wireless Channel for … · Reshma Ann Mathew M.Tech...

Measurements and Characterization of 5G Wireless

Channel for mmWave Massive MIMO System with

Circular Rayline Array Pattern

Reshma Ann MathewM.Tech Scholar

Electronics and CommunicationAmal Jyothi College of Engineering

Kanjirapally, [email protected]

Ria Maria GeorgeAsst. Professor

Electronics and CommunicationAmal Jyothi College of Engineering

Kanjirapally, [email protected]

Abstract—In the current frequency spectrum of cellular com-munication network, there is a shortage of communicationbandwidth. This leads to the exploration of millimeter Wave(mmWave) frequency spectrum which is underutilized. The fifthgeneration (5G) wireless communications uses mmWave commu-nication as the fundamental technology, as the study and char-acterization of different mmWave bands and mmWave MassiveMultiple Input Multiple Output (MIMO) channel measurementsfor large antenna arrays like Uniform Linear Array (ULA)is scarce. In this paper detailed analysis of Circular RaylineArray and mmWave Massive MIMO channel measurementsat 11, 16, 28, and 38 GHz bands has been carried out. Arecently proposed Space-Alternating Generalized Expectation-Maximization(SAGE) algorithm is applied to process the mea-sured data. Important statistical properties, such as AveragePower Delay Profile (APDP), Average Azimuth Profile (AAP),Average Elevation Profile (AEP), Root Mean Square DelaySpread (RMS DS), Elevation Angular Spread (EAS), Power DelayProfile (PDP) and their correlation properties are analysed. Theyare validated over the different antenna arrays like ULA andCircular Rayline Antenna Array. The comparative analysis ofstatistical properties and simulation results indicate that CircularRayline array has lesser distortion than ULA, so Circular Raylineis a better choice than ULA.

Index Terms—mmWave, Massive MIMO, SAGE, ULA, Circu-lar Ryline

I. INTRODUCTION

The inventive and effective utilization of information andcommunication technologies (ICT) is ending up progressivelyvital to enhance the economy of the world. Wireless communi-cation systems are the most basic component in the worldwideICT methodology. The main problem facing by the wirelesssystem designers is increasing demands for high informationrates and portability required by new wireless applications andtherefore has begun research about on fifth generation wirelesssystems (5G Wireless-fifth generation wireless system or fifthgeneration mobile network)[1], [2]. Complete wireless com-munication with no restrictions can be called a real wirelessworld and it has extraordinary transmission speed.The millimter wave (mmWave) communication has been a keyempowering innovation for the fifth generation (5G) wirelesscommunication. The Extremely High Frequency (EHF) range

of 30 GHz to 300 GHz corresponds to mmWave region of theelectromagnetic spectrum, but 10-30 GHz bands also includedas they share some similar propagation characteristics. One ofthe considerable and most vital employments of mmWave isin transmitting a lot of information [1]. In spite of the factthat mmWave permit expansive data transfer capacity, otherfrequencies, infrared and optical wavelengths, allow high datarates and narrow bandwidths. Unlike mmWave, these shorter-wavelength signals suffer from ingestion by fog, tidy andsmoke, so it is preferred to use optical fiber as a wave directingmedium since it is less affected by mist or other air conditions.There are numerous circumstances where optical fibers can’tbe utilized in light of the fact that the transmitters or collectorsare portable, (for example, cell telephones or satellite commu-nication) so radio-wave interchanges, including mmWaves, isgenerally the best decision.The expanding interest for information rates and bandwidthhas inspired studies for next generation mobile communicationsystem. Demand for wireless transmission rate and data trafficof mobile communication is increasing with the developmentof mobile communications. Currently, 5G has become researchfocus since it could support the explosive growth of datatraffic, massive interconnected devices and new applications.Since the spectrum resources in the lower frequency bands isrunning out, millimeter wave frequency attracts many researchattention because of the rich spectrum resources.The main variations as compared to 5g with 4g are the useof much extra spectrum allocations at untapped mmwave fre-quency bands, fairly directional beamforming antennas at boththe cellular device and base station, longer battery existence,lower outage probability, much higher bit prices in largeportions of the coverage place, lower infrastructure price, andhigher aggregate capability for lots simultaneous customers ineach certified and unlicensed spectrum.Massive MIMO wireless communications refers to the conceptof cellular base stations (BSs) with a completely big range ofantennas, and has been proven to doubtlessly permit for ordersof magnitude improvement in spectral and energy efficiencythe usage of simple (linear) processing [10]. Because of

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 4 (2018) Spl.© Research India Publications. http://www.ripublication.com

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the expanding range of antennas the propagation channelcharacteristics have some completely different properties andthey are measured measured by ULA.The combination of mmwave and massive mimo has theability to dramatically enhance wireless access and throughputoverall performance. Such systems advantage from massiveavailable sign bandwidth and small antenna shape issue. Thesystem additionally have advantages in phrases of compactdimensions, power efficiency, flexibility, and adaptivity thatwould make them ideally fitted for 5g conversation structures.In [3] numerous promising technologies for 5G wirelesscommunication systems, such as massive MIMO, Energy-efficient communications, cognitive radio networks, Visiblelight communications, MIMO systems comprises multipleantennas at each the transmitter and receiver are discussed.In [4] discuss the measurement results of the outdoor ur-ban cellular wideband channels at 11 GHz. A wideband24 × 24 MIMO channel sounder having 400 MHz signalbandwidth and dual polarized 12 element uniform circulararrays were used. The measurements at various urban cellularenvironments that are classified into macrocell, microcell andstreet cell were conducted. This paper presents large scalecharacteristics of 11 GHz wideband channels including, Pathlosses, Shadowing, Cell Coverage, Polarization properties anddelay spread which were calculated both at transmitter andreceiver. [5] describes an indoor channel measurement systemat 28 GHz. The measurement system consists of a VNA.PDP, PAP and RMS DS were analyzed. [6] Discussing themeasurement based study of 38 GHz,the 38 GHz mmWavefrequency band is a strong candidate. Compared to lowerfrequency bands, propagation in the 38 GHz band is relativelyunexplored. The study of paper [7] examines the applicationcapability of the SAGE algorithm to mutually estimate therelative delay, incidence azimuth, Doppler frequency. TheAlternating Generalized Expectation Maximization (SAGE)algorithm created by by Fessler and Hero [8] can enhancethe convergence rate considerably. It become observed thatsage algorithm calls for less iterations to attain a stationaryfactor than EM algorithm. [12] 48 element active phased arrayantenna was used to measure massive MIMO channels at44 GHz. [9] conducted 28 GHz measurement in an urbanenivronment with 250 MHz bandwidth.

II. METHODOLOGY

One of the most essential demanding situations ofin advance spectrum’s is the physical scarcity of radiofrequency (RF) spectra allotted for cellular communications.Another difficulty is that the deployment of advancedWIFI technologies comes on the price of high energyconsumption. Different demanding situations are, averagespectral performance, excessive data rate and excessivemobility, seamless coverage, various quality of service(QoS) necessities and fragmented user experience. Differentmmwave configuration will create massive effect onpropagation channel characteristics. The evaluation of variousmmwave bands is scarce, mmwave massive mimo channel

measurements are absent and new propagation characteristicscaused by large antenna arrays have hardly ever been studied.

Problem formulation of this paper is mmWave massiveMIMO channel measurements at 11, 16, 28, and 38 GHzbands. The SAGE algorithm is applied to process themeasurement data. Important statistical properties, such asAPDP, AAP, AEP, RMS DS, EAS, PDP and their correlationproperties are analysed and they are validated over thedifferent antenna arrays like ULA and Circular RaylineAntenna Array.

The main contributions and novelties of this paper:

• This paper studies the propagation characteristics of dif-ferent mmWave frequency bands. The 11, 16, 28, 38 GHzchannel measurements are carried out.

• The measurement data are processed with SAGE al-gorithm. Important statistical characteristics like APDP,AAP, AEP, EAS, RMS DS and correlation properties areobtained.

• New Massive MIMO properties like Power Delay Profile(PDP) is also measured.

• The above properties are validated over ULA and CircularRayline Array.

Fig. 1. Channel estimation

Significance of finding the above parameters:

• System analysis will be easier• Improves the spectral efficiency• Reduces the physical scarcity of radio frequency (RF)

spectra.

A. mmWave Technologies

mmWave communication has been considered as key inno-vation for 5G. The EHF range of 30-300 GHz correspondsto mmWave region of the electromagnetic spectrum, but 10-30 GHz bands also included as they share some comparablepropagation characteristics. One of the best use of mmWavesis in transmitting a lot of information, and it have substantialaccessible data transfer capacity. mmWave permit expansivedata transfer capacity, other frequencies, infrared and opticalwavelengths, allow high data rates and narrow bandwidths.Unlike mmWave, these shorter wavelength signals suffer fromingestion by fog, tidy and smoke, so it is preferred to useoptical fiber as a wave directing medium since it is lessaffected by mist or other air conditions. There are numerouscircumstances where optical fibers can’t be utilized in lightof the fact that the transmitters or collectors are portable, (for


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example, cell telephones or satellite communication) so radio-wave interchanges including mmWaves, is generally the bestdecision.

Fig. 2. Communication in mmWave Frequencies

B. Massive MIMO

The explosive growth of wireless data service, especiallymore and more requirement for high-definition videos, de-mands higher capacities of future wireless communicationsystem to meet this trend. Conventional MIMO is not ascalable technology. Massive MIMO means the use of a verylarge number of several antenna, large number of users areserved simultaneously. Massive MIMO increases the capacity10 times or more and simultaneously improves the radiatedenergy efficiency 100 times. It can be built with inexpensive,low power components and it also enables a significant reduc-tion of latency on the air interface.Massive mimo or huge antenna array system has the function-ality of substantially enhancing spectral performance, strengthefficiency and system robustness [10] [11].in an exceedinglytypical massive MIMO system, single antenna mobile stations(MSs) communicate with a BS prepared with a large numberof antennas. Because of the increasing variety of antennas, thepropagation channel characteristics have a few new propertiesthat must be measured.

The combination of mmWave and massive mimo has theability to dramatically:

• Improve wireless access & Throughput performance.• Systems benefit from large available signal bandwidth

and small antenna form factor.The systems also have advantages in terms of:

• Compact dimensions• Energy efficiency• Flexibility• Adaptivity

III. SYSTEM MODEL

A. Block Diagram

The proposed system as shown in Fig. 4. In earlier mea-surements and studies are relatively few and not sufficient tofully characterize the mmWave and Massive MIMO channelmeasurements should be conducted at mmWave bands. In thispaper, Massive MIMO channel measurements at 11, 16, 28 and38 GHZ bands by using ULA and circular Rayline array arecarried out. The Combination of mmWave and massive MIMOare given as the input to the signal. Initially ULA is given as

Fig. 3. Massive MIMO

the antenna array and compares the propagation characteristicsof different mmWave frequency bands. The 11, 16, 28 and 38GHz channel measurements are carried out. SAGE algorithmis used for processing the channel measurements data. In thesecond part of the block diagram circular rayline antenna arrayis used to carried out the propagation characteristics. Herealso SAGE algorithm is used for processing the data. Boththe result are compared and the array which gives the bestresult is selected.

Spatial characteristics like APDP, AAP, AEP, EAS, Cor-relation properties are obtained by SAGE Algorithm. NewMassive MIMO property like PDP are validated for mmWavebands.

Fig. 4. Proposed Block Diagram

B. ULA & Circular Rayline Antenna Array

An antenna array is a set of two or more antennas. Thesignal from the antennas are mixed or processed to achieveadvanced overall performance over that of a single antenna.The idea of sub arraying arises from the requirement of con-temporary antenna for high resolution.Subarray is employedto reduce the beamforming complexity. Uniform linear arraycomprises antenna element organized on a line. Elements ofthe array are uniformly spaced. An array of equal factors withequal magnitudes and with a progressive phase is known asuniform linear array. When the quantity of elements in an arrayis higher, it gives higher directivity.Antenna elements are arranged around a circular ring andfor each frequency different beam pattern is formed. Unlike


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Uniform linear arrays, Circular Rayline arrays can provide a2D angular scan, both horizontally & vertical. Unlike lineararrays, a circular array can scan horizontally for 360 degreewith no distortions.

C. SAGE Algorithm

The SAGE algorithm is an extension of EM algorithm.It provides quicker convergence than EM. Algorithm attainsmany hidden information area instead of only one completeinformation area. EM algorithms update all parameters con-currently, which has drawbacks:

1 Slow convergence2 Difficult maximization steps due to coupling when

smoothness penalties are used.Maximum Likelihood (ML) has asymptotically best statisticalperformance and is in some sense robust against small samplenumber and coherent source signals. Unfortunately the highcomputational complexity associated with general implemen-tation of ML makes it less enticing in apply. It’s a crucial anddifficult task to find quicker, more efficient implementationsof ML. One common approach to beat this procedure issue isthe EM algorithmic. EM algorithmic enjoys two favourableproperties: easy implementation and stability. However, inseveral cases it suffers from slow convergence. Based on a sim-ilar plan of information augmentation, the SAGE algorithmicdeveloped by Fessler and Hero will improve the convergencerate considerably.Advantages of SAGE Algorithm:

• Compress composite path• Compute edge statistics• Estimate mean and standard deviation

The high-resolution SAGE algorithm is widely used for wire-less channel parameter estimations. The algorithm can jointlyestimate the complex amplitude, delay, azimuth angle andelevation angle of the Multi Path Components (MPCs). Thereceived signal y(t) is assumed to consist of a finite numberL of specular plane waves, i.e., [1]

y(t) = ΣLl=1S(t; Θl) +

√N0

2N(t) (1)

S(t; Θl) = αlC(Ωl)u(t− τl) (2)

C(Ωl) = [C(Ωl), ......, CN2Ωl]T (3)

where u(t) is the transmitted signal. The lth MPC is S(t;Θl).N(t) is the standard N2 × 1 dimensional complex whiteGaussian noise with spectral height of N0, the steering vectorof the Rx array is C(Ωl).

In the SAGE algorithm, the number of MPCs L is usuallypredefined large enough to capture all significant paths. Somevalues for the numbers of MPCs have been investigated. Whenthe number of MPCs equals 100, a good trade-off betweenaccuracy and computational complexity can be achieved [1].

Fig. 5. Important statistical properties found by SAGE Algorithm

D. Important Statistical Properties Found by SAGE Algorithm

Power Delay Profile (PDP):PDP offers the intensity of a signal obtained through a

multipath channel as a function of time delay. The time delayis the distinction in travel time between multipath arrivals. Itcan be measured empirically and can be used to extract certainchannel parameters.

The PDP is the square of the amplitude of the h(n,k) ie, [1]

PDP = |h(n, k)|2 (4)

Average Power Delay Profile (APDP):The Average power delay profile (APDP) gives the average

intensity of a signal received through a multipath channel as afunction of time delay. Power Delay Profile is wont to estimatethe common power of a multipath channel, measured from theprimary signal that strikes the receiver to the last signal whosepower level is higher than certain threshold.

APDP = (1

N2)ΣN2

n=1|h(n, k)|2 (5)

Average Azimuth Profile & Average Elevation Profile Equa-tions: The Azimuth defined as a horizontal angle measuredclockwise from a north base line or meridian. Azimuth has alsobeen more generally defined as a horizontal angle measuredclockwise from any fixed reference plane or easily establishedbase direction line.

AAP = ΣLl=1|α1|2δ(φ− φ1) (6)

AEP equation is given by:

AEP = ΣLl=1|α1|2δ(Θ−Θ1) (7)

where δ() is the Dirac delta function.H(n, k) is the Cumulative Transfer Function (CTF) matrix.

The inverse fourier transform of H(n,k) ie,

h(n, k) = IFFT (H(n, 1 : K)), n = 1, ....., N2

Correlation Properties:


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Ccorrelation is a statistical degree that suggests the quantityto which two or extra variables vary together Correlationcoefficient is a factual measure of how much changes tothe estimation of one variable foresee the change to theestimation of another. At that point when the fluctuation of onevariable dependably predicts a comparative change in anothervariable. The correlation separation can be characterized asthe distance when the correlation coefficient is equivalentto 0.5. The correlation coefficients are primarily influencedby the transmitter area. Compared to the higher groups of28GHz and 38GHz, the lower groups of 11GHz and 16GHzthe spacing steps are larger and the correlation coefficientstends to decrease slower.

RMS DS:The RMS DS is the second order statistics to characterize

the channel dispersion in delay and angular domains. TheRMS DS is related with channel coherence bandwidth andinter-symbol interference. The maximum delay time spread isthe total time interval during which reflections with significantenergy arrive. The RMS DS is the standard deviation value ofthe delay of reflections, weighted proportional to the energyin the reflected waves.

IV. RESULTS AND DISCUSSION

In this Section, the Experimental results are discussed. Forthis paper, Circular Rayline antenna array is considered insteadof ULA antenna array in [1]. Using this implementation it canimprove the

• Throughput• Transmission Rate• Power Consumption reduction• Improve the correlation Coefficients

A. Experimental Results for ULA

Fig. 6. Transmit Array for ULA

The Transmit array patterns and Receive array patterns areobtained at 16 GHz, 28 GHz and 38 GHz in the Figures 6

Fig. 7. Receive Array for ULA

Fig. 8. Correlation Coefficient for ULA

and 7 respectively. Here in transmit array the minimum powergain obtained is -20 dB and the maximum value obtained inthe range of -70 dB to -80 dB, similar results were obtainedfor channel array and receive array. From this observation itis clear that high power spectral density is needed for ULA.

Correlation is a statistical degree that suggests the quantityto which two or extra variables vary together. Compared with28 GHz and 38 GHz higher bands, the spacing steps arelarger and in 11 GHz and 16 GHz lower bands the correlationcoefficients tends to decrease slower. Figure 8 shows that thecorrelation coefficient value for ULA is less than 1.

Figure 9 shows the Massive MIMO property ie PDP. Foreach and every antenna position, different delay values aregot and that matrix computation is shown by contour graph.In ULA the PDP variation obtained is a high value of 45 ns.


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Fig. 9. PDP Variation for ULA

Fig. 10. Profile parameters for ULA

Figure 10 shows the result of important statistical propertiessuch as APDP, AAP, AEP, RMS DS,EAS that are carried outby SAGE algorithm. From equation (5) APDP is obtained andfrom equation (6), (7), (4) AAP, AEP, PDP are obtained. Hereprofile parameter transmission rate is getting less than 14.

B. Experimental Results of Circular Rayline Array Pattern

Figure 11 shows the beam pattern formation by usingindividual raylines. Circular pattern is obtained by multipleline of rayline, so it is called circular pattern RaylinesMIMO Radar. By using rayline circular pattern channelparameters are improved. Earlier channel parameters werefound by ULA. Rayline is for individual frequency, for eachfrequency different beampattern are formed ie basically calledrayline circular pattern. Figure 12 shows that for differentwavelength, different beam frequencies and different beam

pattern are obtained. Totally eight different patterns arehandled in Circular Rayline Antenna Array. In ULA only onebeam pattern is considered.Figure 13 shows the transmit array for circular rayline antennaarray. Here the power gain is very much less, which indicatesthat the system is energy efficient when the value that areobtained as low as possible. In ULA minimum value is -50dB and maximum value is -80 dB, here (circular raylineantenna array) -250 dB is obtained which indicates the powerspectral density is much reduced. Once the power spectraldensity is reduced highest correlation value and transmissionrate is obtained.

Figures 14 and 15 shows the comparison PDP variationand Profile Parameters for Circular Rayline antenna arrayand ULA. In ULA PDP variation is 45 ns and in circulararray PDP is 30 ns, Power Gain is also obtained is lowwhich automatically improving the profile parameters. In thisimplementation the profile parameter value is obtained nearly15. The APDP, AAP, AEP, EAS, DS are obtained for 38GHz band frequency. RMS DS is the important second orderstatistics to characterize the channel dispersion in delay andit is also related with channel coherence bandwidth and intersymbol interference.

Fig. 11. Beam Patterns of MIMO Radar

Fig. 12. Number of elements=64, d/ λ= 0.5

CONCLUSIONmmWave communication has been considered as a key

technology for the 5G. In this paper Massive MIMO channel


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Fig. 13. Transmit Array for Circular Rayline Antenna Array

Fig. 14. Power Delay Profile for Circular Rayline Antenna Array and ULA

measurements at 11, 16, 28, 38 GHZ frequency bands arecarried out. The measurement data have been processed withSAGE algorithm and important statistical properties, such asAPDP, AAP, AEP, RMS DS, EAS, PDP and their corre-lation properties are analysed and they are validated overthe different antenna arrays like ULA and Circular RaylineAntenna Array are used. Transmit array pattern, Channel arraypattern, Receive array pattern and power gain are also obtainedin these two antenna arrays. The comparative analysis ofstatistical properties indicate that Circular Rayline array haslesser distortion than ULA, so Circular Rayline is a betterchoice than ULA.

REFERENCES

[1] Jie Huang, Cheng-Xiang Wang, “Multi-Frequency mmWave MassiveMIMO Channel Measurements and Characterization for 5G WirelessCommunication Systems,”IEEE Journal, Vol. 35, No. 7, July 2017.

[2] T. S. Rappaport et al.,“ Millimeter wave mobile communications for 5Gcellular: It will work, ” IEEE Access, vol. 1, pp. 335349, May 2013.

[3] C.-X. Wang et al., “Cellular architecture and key technologies for 5Gwireless communication networks,” IEEE Commun. Mag., vol. 52, no.2, pp. 122130, Feb. 2014.

[4] M. Kim, J.-I. Takada, Y. Chang, J. Shen, and Y. Oda, ”Large scalecharacteristics of urban cellular wideband channels at 11 GHz,” in Proc.EuCAP, Lisbon, Portugal, Apr. 2015, pp. 14.

Fig. 15. Profile parameter for Circular Rayline Antenna Array and ULA

[5] M. Lei, J. Zhang, T. Lei, and D. Du, ”28-GHz indoor channel measure-ments and analysis of propagation characteristics,” in Proc. PIMRC,Washington, DC, USA, Sep. 2014, pp. 208212.

[6] I. Rodriguez et al., ”Analysis of 38 GHz mmWave propagation charac-teristics of urban scenarios,” in Proc. Eur. Wireless, Budapest, Hungary,May 2015, pp. 18.

[7] B. H. Fleury, M. Tschudin, R. Heddergott, D. Dahlhaus, and K. IngemanPedersen, ”Channel parameter estimation in mobile radio environmentsusing the SAGE algorithm,” IEEE J. Sel. Areas Commun., vol. 17, no.3, pp. 434450, Mar. 1999.

[8] Pei Jung Chung and Johann F. Bohme, ”Comparative ConvergenceAnalysis of EM AND SAGE Algorithms in DOA Estimation,” in Dept.of Electrical Engineering and Information Science Ruhr-UniversitatBochum, 44780 Bochum, Germany. Eur. Wireless, Budapest, Hungary,May 2015, pp. 18.

[9] S. Hur et al., ”Wideband spatial channel model in an urban cellularenvironments at 28 GHz,” in Proc. EuCAP, Lisbon, Portugal, Apr. 2015,pp. 15.

[10] L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang,”An overview of massive MIMO: Benefits and challenges,” IEEE J.Sel. Topics Signal Process, vol. 8, no. 5, pp. 742758, Oct. 2014.

[11] L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. H. Wei,D. Wang, J. Wang, and X. You, ”Impact of RF mismatches on theperformance of massive MIMO systems with ZF precoding”, vol. 59,no. 2, pp. 2639, Feb. 2016

[12] A. Taira et al., ”Performance evaluation of 44 GHz band massiveMIMO based on channel measurement,” in Proc. Globecom, San Diego,CA, USA, Dec. 2015, pp. 16.

Reshma Ann Mathew: Received the B.Tech degree in Electronics andCommunication from Mahatma Gandhi University, Kottayam, India, in2012. Currently pursing the M.Tech degree in communication engineer-ing from APJ Abdul Kalam Technological University, India. The currentresearch interest include millimeter Wave and Massive MIMO channelmeasurements.


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